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The “cluster of six”

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Unsupervised machine learning Hansard reports what’s said in the UK Parliament, sets out details of divisions, and records decisions taken during a sitting. The hansard R package provides functions to import its data. Using the Hansard API (Application Programming Interface), we’ll apply unsupervised machine learning to analyze the voting patterns of 219 Labour Members of Parliament (MPs). We’ll consider all divisions (results of the votes) in the UK House of Commons since the 2017 general election. Supervised machine learning makes predictions from labeled training data. The unsupervised flavour looks for hidden structure in “unlabeled” data, i.e. a classification or categorisation not included in the observations. Hierarchical clustering will identify a cluster of six MPs as the most “distant” from the wider party. The full methodology, including the code, is published here. This extended narrative confirms the suitability of the data for clustering; reviews…
Original Post: The “cluster of six”